# What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

arXiv: https://arxiv.org/abs/2201.08550 · HTML: https://mlsanity.com/paper/2201.08550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 213 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images](https://mlsanity.com/paper/2001.01599/) (2020)
- [Machine learning and deep learning](https://mlsanity.com/paper/2104.05314/) (2021)
- [GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection](https://mlsanity.com/paper/2104.14528/) (2021)
- [A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks](https://mlsanity.com/paper/2003.12255/) (2020)
- [Deep supervision with additional labels for retinal vessel segmentation task](https://mlsanity.com/paper/1806.02132/) (2018)
- [ScanNet: A Fast and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Images](https://mlsanity.com/paper/1707.09597/) (2017)
- [DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set](https://mlsanity.com/paper/2009.08123/) (2020)
- [A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images](https://mlsanity.com/paper/1710.01249/) (2017)
- [Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning](https://mlsanity.com/paper/1811.02668/) (2018)
- [Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1608.01658/) (2016)
- [Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks](https://mlsanity.com/paper/1612.03217/) (2016)
- [Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases](https://mlsanity.com/paper/1904.09075/) (2019)
- [RCNN for Region of Interest Detection in Whole Slide Images](https://mlsanity.com/paper/2009.07532/) (2020)
- [Automatic breast cancer grading in lymph nodes using a deep neural network](https://mlsanity.com/paper/1707.07565/) (2017)

## Built on by (most cited first, 1 of 1)

- [Unmasking Biases and Reliability Concerns in Convolutional Neural Networks Analysis of Cancer Pathology Images](https://mlsanity.com/paper/2603.12445/) (2026)

## Abstract (arXiv)

In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some more potential methods are proposed, and further prospects are made.